深度学习，语义分割和检测

Deep learning and convolutional networks, semantic image segmentation, object detection, recognition, ground truth labeling, bag of features, template matching, and background estimation

计算机视觉的工具箱™支持金宝appseveral approaches for image classification, object detection, and recognition, including:

Deep learning and Convolutional neural networks (CNNs)
包的功能
模板匹配
Blob analysis
Viola-Jones algorithm
Interactive apps for ground truth labeling

A CNN is a popular deep learning architecture that automatically learns useful feature representations directly from image data. Bag of features encodes image features into a compact representation suitable for image classification and image retrieval. Template matching uses a small image, or template, to find matching regions in a larger image. Blob analysis uses segmentation and blob properties to identify objects of interest. The Viola-Jones algorithm uses Haar-like features and a cascade of classifiers to identify objects, including faces, noses, and eyes. You can train this classifier to recognize other objects.

Highlighted Topics

Image and Video Labeling
对象检测，语义分割和图像分类的交互式图像和视频标记
语义细分
Semantic image segmentation
Object Detection using Deep Learning
Perform classification, object detection, transfer learning using convolutional neural networks (CNNs, or ConvNets)
Object Detection Using Features
Detect faces and pedestrians, create customized detectors
Image Category Classification and Image Retrieval
为图像分类和基于内容的图像检索（CBIR）系统创建一袋视觉单词
Optical Character Recognition (OCR)
Recognize text using optical character recognition